Motion estimation in video sequences is a classical intensive computational task that is required for a wide
range of applications. Many different methods have been proposed to reduce the computational complexity,
but the achieved reduction is not enough to allow real time operation in a non-specialized hardware. In this
paper an efficient selection of singular points for fast matching between consecutive images is presented, which
allows to achieve real time operation. The selection of singular points lies in finding the image points that
are robust to the noise and the aperture problem. This is accomplished by imposing restrictions related to
the gradient magnitude and the cornerness. The neighborhood of each singular point is characterized by a
complex descriptor vector, which presents a high robustness to illumination changes and small variations in the
3D camera viewpoint. The matching between singular points of consecutive images is performed by maximizing
a similarity measure based on the previous descriptor vector. The set of correspondences yields a sparse motion
vector field that accurately outlines the image motion. In order to demonstrate the efficiency of this approach, a
video stabilization application has been developed, which uses the sparse motion vector field as input. Excellent
results have been efficiency of the proposed motion
estimation technique.